CN116400412A - Seismic horizon extraction method and device, electronic equipment and computer storage medium - Google Patents
Seismic horizon extraction method and device, electronic equipment and computer storage medium Download PDFInfo
- Publication number
- CN116400412A CN116400412A CN202310429289.8A CN202310429289A CN116400412A CN 116400412 A CN116400412 A CN 116400412A CN 202310429289 A CN202310429289 A CN 202310429289A CN 116400412 A CN116400412 A CN 116400412A
- Authority
- CN
- China
- Prior art keywords
- seismic
- horizon
- target
- determining
- dip angle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000605 extraction Methods 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 claims abstract description 40
- 238000005314 correlation function Methods 0.000 claims abstract description 19
- 238000004891 communication Methods 0.000 claims description 17
- 238000000354 decomposition reaction Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 4
- 239000013598 vector Substances 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 12
- 238000004364 calculation method Methods 0.000 description 6
- 230000015572 biosynthetic process Effects 0.000 description 5
- 238000005755 formation reaction Methods 0.000 description 5
- 238000005070 sampling Methods 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000001914 filtration Methods 0.000 description 3
- 238000009499 grossing Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000002939 conjugate gradient method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000011022 operating instruction Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/301—Analysis for determining seismic cross-sections or geostructures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/70—Other details related to processing
- G01V2210/74—Visualisation of seismic data
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a seismic horizon extraction method, a seismic horizon extraction device, electronic equipment and a computer storage medium. Wherein the method comprises the following steps: selecting a preset control point from a target horizon of the seismic data, and intercepting the seismic data based on the target horizon and the preset control point to obtain the target seismic data; determining a local dip angle of the seismic image according to the structure tensor of the target seismic data; performing linear interpolation on the target seismic data according to a preset control point to obtain an initial seismic horizon; selecting a preset number of seismic channels according to the initial seismic horizon, and determining a multi-scale dip angle based on a cross-correlation function between every two preset number of seismic channels; and obtaining a final seismic horizon according to the local dip angle and the multi-scale dip angle. According to the scheme, a more accurate horizon extraction effect can be achieved.
Description
Technical Field
The invention relates to the technical field of seismic horizons, in particular to a seismic horizon extraction method, a device, electronic equipment and a computer storage medium.
Background
Extracting horizons from seismic images is one of the basic steps in seismic data interpretation, helping to build subsurface formations and formation models, seismic topography analysis. Seismic horizons refer to planes that match the planes of a constant geologic time, representing the same period of geology. Thus, seismic horizons can be used to construct chronostratigraphic models and are important for identifying formations and stratigraphic features, as discontinuities or terminations of horizons typically represent formations such as faults, river courses, unconformities, salt dome boundaries, etc., and horizons can also be used to analyze ancient depositional environments and topographical features.
Since the data of seismic imaging can be very large and complex (particularly three-dimensional data), manually extracting seismic horizons is a very time-consuming and error-prone process. To address this problem, researchers have developed a variety of automated or semi-automated methods to interpret seismic horizons:
1. methods based on waveform similarity. The method mainly uses coherence or relativity between adjacent channels to gradually generate the seismic horizon by tracking similar waveforms from seed points by a recursive method. Such methods typically follow relatively small structural changes, choosing locally optimal solutions, and thus can effectively reveal some detailed structures. However, such methods may not be able to extract consistent horizons when passing through some discontinuities such as noise and faults.
2. A method based on the instantaneous phase of the earthquake. This approach assumes that the seismic horizon follows a curve (two-dimensional) or surface (three-dimensional) of relatively constant instantaneous phase throughout the seismic image, first obtaining a relative geologic time volume from the instantaneous phase of the seismic, and then extracting a relative geologic time contour to generate the seismic horizon. Such methods, while globally optimal, typically produce a relatively smooth horizon, lacking detailed geological information. In addition, a significant amount of manual interpretation work is required in dealing with seismic discontinuities, including faults and unconformities.
3. A method based on local reflection slope of a seismic image. The method mainly utilizes structure tensor, coherence scanning, two-dimensional log-Gabor filtering, dynamic image scanning and the like to estimate the local reflection slope of the seismic image. Such methods based on local reflection slopes of seismic images are robust to noise, but they cannot track large displacements caused by faults well, and the seismic horizons extracted by such methods cannot reveal detailed geologic structures due to averaging effects on local features.
4. A horizon extraction method based on a fault-removed seismic image. Since the local slope of the seismic image of the former method cannot track the correct reflection axis information at locations across the fault, many methods based on local reflection slope of the seismic image cannot extract the correct horizon at locations across the fault. So to solve this problem, it has been proposed by the learner to remove faults from the seismic image before extracting horizons, or to extract horizons by using the addition of manual control points (typically placed on both sides of the fault) as constraints.
Disclosure of Invention
The present invention has been made in view of the above problems, and it is an object of the present invention to provide a seismic horizon extraction method, apparatus, electronic device and computer storage medium that overcome or at least partially solve the above problems.
According to one aspect of the present invention, there is provided a seismic horizon extraction method comprising:
selecting a preset control point from a target horizon of the seismic data, and intercepting the seismic data based on the target horizon and the preset control point to obtain target seismic data;
determining a local dip angle of the seismic image according to the structure tensor of the target seismic data;
performing linear interpolation on the target seismic data according to the preset control points to obtain an initial seismic horizon;
selecting a preset number of seismic channels according to the initial seismic horizon, and determining a multi-scale dip angle based on a cross-correlation function between every two preset number of seismic channels;
and obtaining a final seismic horizon according to the local dip angle and the multi-scale dip angle.
According to another aspect of the present invention, there is provided a seismic horizon extraction apparatus comprising:
the control point selection module is used for selecting a preset control point from a target horizon of the seismic data, and intercepting the seismic data based on the target horizon and the preset control point to obtain the target seismic data;
the local dip angle determining module is used for determining the local dip angle of the seismic image according to the structure tensor of the target seismic data;
the initial horizon determination module is used for carrying out linear interpolation on the target seismic data according to the preset control points to obtain an initial seismic horizon;
the multi-scale dip angle determining module is used for selecting a preset number of seismic channels according to the initial seismic horizon and determining multi-scale dip angles based on cross-correlation functions between every two preset number of seismic channels;
and the seismic horizon determination module is used for obtaining a final seismic horizon according to the local dip angle and the multi-scale dip angle.
According to another aspect of the present invention, there is provided an electronic apparatus including: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the seismic horizon extraction method.
According to another aspect of the present invention, there is provided a computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the seismic horizon extraction method of the present invention.
According to the seismic horizon extraction method, the electronic equipment and the computer storage medium disclosed by the invention, a preset control point is selected from a target horizon of seismic data, the seismic data is intercepted based on the target horizon and the preset control point to obtain the target seismic data, the local dip angle of the seismic image is determined according to the structure tensor of the target seismic data, the linear interpolation is carried out on the target seismic data according to the preset control point to obtain an initial seismic horizon, a preset number of seismic channels are selected according to the initial seismic horizon, the multi-scale dip angle is determined based on the cross-correlation function between every two preset number of seismic channels, and the final seismic horizon is obtained according to the local dip angle and the multi-scale dip angle, so that a more accurate horizon extraction effect can be realized.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a flow chart of a seismic horizon extraction method according to one embodiment of the invention;
FIG. 2 is a schematic flow chart of a seismic horizon extraction method according to a second embodiment of the invention;
FIG. 3 is a schematic view of two-dimensional seismic data in a seismic horizon extraction method according to a second embodiment of the invention;
FIG. 4 is a schematic view of a local dip angle of two-dimensional seismic data in a seismic horizon extraction method according to a second embodiment of the invention;
FIG. 5 is a schematic diagram showing linearity of two-dimensional seismic data in a seismic horizon extraction method according to a second embodiment of the invention;
FIG. 6 is a schematic diagram of a seismic trace of two-dimensional seismic data in a seismic horizon extraction method according to a second embodiment of the invention;
FIG. 7 is a schematic diagram of a seismic trace waveform after time-ordered flattening in a seismic horizon extraction method according to a second embodiment of the invention;
fig. 8 is a schematic diagram illustrating cross-correlation of seismic traces in a seismic horizon extraction method according to a second embodiment of the invention;
FIG. 9 is a diagram of a seismic trace after dynamic adjustment of two-dimensional seismic data in a seismic horizon extraction method according to a second embodiment of the invention;
FIG. 10 is a schematic diagram of a dynamically adjusted time-ordered flattened seismic trace waveform in a seismic horizon extraction method according to a second embodiment of the invention;
FIG. 11 is a schematic diagram illustrating an iterative seismic horizon extraction process in a seismic horizon extraction method according to a second embodiment of the invention;
FIG. 12 is a schematic diagram of a seismic horizon extraction apparatus according to a second embodiment of the invention;
fig. 13 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Example 1
Fig. 1 shows a flowchart of a seismic horizon extraction method according to a first embodiment of the invention. The execution body of the embodiment is the seismic horizon extraction apparatus provided by the embodiment of the invention, and the apparatus can be implemented in software or hardware. As shown in fig. 1, the method includes:
step S11, selecting a preset control point from a target horizon of the seismic data, and intercepting the seismic data based on the target horizon and the preset control point to obtain the target seismic data.
The seismic data can be a two-dimensional seismic section or a three-dimensional seismic data volume, and the interested horizon is determined by observing the seismic data and is taken as a target horizon.
After the target horizon is explicitly selected, information such as morphology and distribution of the horizon needs to be observed, and approximate positions and number of pickup control points are determined. The location and number of control points may be determined based on a variety of factors such as the resolution of the seismic data, the target horizon, the geologic background, and the like. Then, the control points are manually selected and checked on the seismic data, and the position information of the control points is saved after confirming that the control points are correct. The control points may be further adjusted and added if necessary. Typically, the control points are distributed along the direction of the seismic data construction.
According to the target horizon and the selected control points, the seismic data can be intercepted along the depth direction or the time direction according to the needs by combining the size of the seismic data, and the target horizon and the selected control points are ensured to be contained in the intercepted seismic data in the intercepting process.
Step S12, determining the local dip angle of the seismic image according to the structure tensor of the target seismic data.
Wherein the structure tensor is a structure tensor representing the seismic data. By computing the structure tensor, the structural features surrounding each pixel in the seismic data can be obtained and used to estimate the seismic structure and formation direction.
Specifically, for two-dimensional seismic data, its structure tensor T (x) is expressed as:
g 1 (x) And g 2 (x) Representing the gradient of the target seismic data in the z-direction and the x-direction, respectively. The structure tensor has 3 independent components, each of which is a two-dimensional seismic image of exactly the same size as the target seismic data.<·>Representing the application of two-dimensional smoothing filtering to the individual components of the structure tensor.
For three-dimensional seismic data, its structure tensor T (x) is represented as:
g 1 (x) And g 2 (x) Representing the gradient of the target seismic data in the z-direction and the x-direction, respectively. The structure tensor has 6 independent components, each of which is a three-dimensional seismic image of exactly the same size as the truncated seismic data.<·>Representing the application of three-dimensional smoothing filtering to the individual components of the structure tensor.
And S13, performing linear interpolation on the target seismic data according to preset control points to obtain an initial seismic horizon.
Specifically, according to the selected control points, linear interpolation is performed on the target seismic data to obtain an initial seismic horizon. The seismic horizons extracted in the later steps can be optimally adjusted based on the initial horizons.
And S14, selecting a preset number of seismic channels according to the initial seismic horizon, and determining a multi-scale dip angle based on a cross-correlation function between every two preset number of seismic channels.
Specifically, before performing a multi-scale trace cross-correlation algorithm, different pairs of traces need to be selected. For the intercepted seismic data, partial seismic traces are used to calculate the correlation thereof, wherein the distance between the ith and jth seismic traces is within a preset range, and the extracted seismic traces are approximately uniformly distributed in space. And selecting a certain number of sampling points, such as 40 sampling points, up and down for the multi-scale seismic traces by taking the currently picked horizon position as the center, so that the calculation efficiency of cross correlation can be effectively reduced. The multi-scale dip angle information can be obtained by calculating the correlation of the multi-scale seismic traces.
When the seismic channels are selected, the seismic channels corresponding to the coarse grid can be selected, the seismic channels are intercepted in the time and depth directions, and the intercepted seismic channels are used for subsequent cross-correlation.
And S15, obtaining a final seismic horizon according to the local dip angle and the multi-scale dip angle.
Specifically, global fitting may be performed based on local dip and multi-scale dip of the seismic image, e.g., creating an equation, iteratively extracting horizons.
Therefore, in order to avoid memory and calculation consumption in the process of calculating the local dip angle of the earthquake, the memory and calculation consumption is reduced by intercepting the earthquake data, the dip angle obtained by multi-scale cross-correlation calculation can accurately and effectively correlate reflections of two sides of a fault or other discontinuous structures, accurate pickup of the horizon is realized, accurate horizon pickup can be realized for both two-dimensional earthquake data and three-dimensional earthquake data, phase consistency is followed, and fault pickup is accurately crossed.
In an alternative embodiment, step S12 specifically includes:
step S121, eigenvalue and eigenvector decomposition is performed on the structure tensor of the target seismic data.
Specifically, by decomposing the eigenvalue and eigenvector of the structure tensor T (x), the eigenvalue of the structure tensor of the two-dimensional seismic data is expressed as:
T(x)＝λ u u(x)u T (x)+λ v v(x)v T (x)
wherein u (x) and v (x) respectively represent normalized eigenvectors, and the corresponding eigenvalues thereof are respectively lambda u And lambda (lambda) v . Let lambda be u ≥λ v The corresponding eigenvector u (x) is perpendicular to the seismic reflection and v (x) is parallel to the seismic reflection.
For three-dimensional seismic data, the feature decomposition is expressed as:
T(x)＝λ u u(x)u T (x)+λ v v(x)v T (x)+λ w w(x)w T (x)
wherein u (x), v (x) and w (x) respectively represent normalized eigenvectors, and their corresponding eigenvalues are λ respectively u 、λ v And lambda (lambda) w . Wherein the characteristic value lambda u ，λ v And lambda (lambda) w The normalized eigenvector u (x) is perpendicular to the seismic reflection, while eigenvectors v (x) and w (x) are in planes locally parallel to the seismic reflection.
Step S122, determining the local dip angle of the seismic image according to the feature vector.
For example, assuming that the eigenvector u (x) of the seismic image is always downward, the reflection inclination angles in the x-direction and the y-direction, i.e., the local inclination angles, can be calculated as:
wherein u is 1 (x)、u 2 (x) And u 3 (x) The feature vectors are in the z direction (vertical direction), x direction (horizontal direction) and y direction (vertical direction), respectively.
In an alternative embodiment, step S15 specifically includes:
step S151, determining the weight according to the fault or linearity or flatness of the target seismic data.
In particular, faults may be picked up for the volume of seismic data, which may act as constraints for discontinuities in determining horizons. Alternatively, linearity (for two-dimensional seismic data) or flatness (for three-dimensional seismic data) is calculated for seismic data, which is a seismic attribute that is meant to refer to the rate of change of amplitude between two points in the direction of the same axis of the seismic in a plane parallel to the axis of the seismic component. It can be used to evaluate the planarity of the source fault, with lower values indicating more prominent faults.
Step S152, determining a seismic horizon extraction equation according to the weight, the local dip angle and the multi-scale dip angle.
Specifically, a related horizon extraction equation is established according to the weight, the seismic local dip angle and the multi-scale dip angle, and the seismic horizons are extracted iteratively.
The corresponding equations for two-dimensional seismic data are as follows:
wherein w (x, z) i ) Representing the weight corresponding to the ith iteration; μ represents a weight factor; z i+1 Representing the picked seismic horizon after the i+1st iteration; p (x, z) i ) Representing the local dip for the ith iteration; p (x) k ,z i ) Representing the multiscale tilt angle of the ith iteration.
Wherein the first term of the equation represents a fit of the seismic local dip field, wherein p (x; h, z) represents a fit of the multi-scale local dip field; the last term of the equation is a regularization term for the picked horizon z i And applying vertical smoothing, and iteratively solving a least square solution of the equation by using a conjugate gradient method to obtain a final seismic horizon.
For three-dimensional seismic data, the horizon extraction equation for the local dip and multi-scale dip global fit of the seismic image is as follows:
the first two formulas in the equation represent the fitting of local dip angles in the vertical direction and the horizontal direction, the 3 rd to 4 th formulas represent the fitting of multi-scale dip angles in the vertical direction and the horizontal direction, and the last formula represents a regularization item.
And step S153, obtaining a final seismic horizon according to the seismic horizon extraction equation.
In an alternative embodiment, the method further comprises:
and S16, carrying out eigenvalue and eigenvector decomposition on the structure tensor of the target seismic data.
Referring specifically to step S121, a detailed description thereof is omitted.
And S17, determining the linearity or flatness of the seismic image according to the characteristic value.
Based on the above step S121, the characteristic value λ u 、λ v And lambda (lambda) w The anisotropy of the seismic reflection, such as linearity (two-dimensional seismic data) and flatness (three-dimensional seismic data) c (x), is advantageously measured as a seismic attribute, which is defined as the rate of change of amplitude between two points in the direction of the same axis of the seismic in a plane parallel to the axis of the seismic component. It can be used to evaluate the planarity of the source fault, with lower values indicating more prominent faults. c (x) is expressed as:
c (x) (0.ltoreq.c (x). Ltoreq.1) is typically used to highlight discontinuities in seismic data where the magnitude of c (x) is lower near the fault. The linearity here can also be replaced by a picked-up fault as a representation of the discontinuity.
In an alternative embodiment, step S14 specifically includes:
in step S141, a seismic trace is determined based on control points in the initial seismic horizon and a preset interval.
Specifically, before performing a multi-scale trace cross-correlation algorithm, different pairs of traces need to be selected. For the intercepted seismic data, namely the target seismic data, partial seismic traces are adopted to calculate the correlation, wherein the distance between the ith and the jth seismic traces is in a preset range, and the extracted seismic traces are approximately uniformly distributed in space. And selecting a certain number of sampling points, such as 40 sampling points, up and down for the multi-scale seismic traces by taking the currently picked horizon position as the center, so that the calculation efficiency of cross correlation can be effectively reduced.
Step S142, screening out the seismic traces at the fault positions when the preset number of seismic traces are selected.
The seismic channels at fault positions are avoided when the seismic channels are subjected to multi-scale cross-correlation, and the effectiveness of the cross-correlation of the seismic channels can be ensured.
In an alternative embodiment, step S14 specifically includes:
step S141, arranging a preset number of seismic channels according to a time sequence, and performing cross-correlation on each seismic channel and other seismic channels to obtain a correlation function.
Specifically, two seismic channels are aligned on a time axis by adopting a dynamic time sorting method, namely, the seismic channels are arranged according to a time sequence, and each seismic channel is cross-correlated with all other seismic channels to obtain a series of correlation functions.
In step S142, the time offset between each seismic trace and other seismic traces is determined according to the correlation function, and the pair-wise correlation between all time points or depth points between every two seismic traces is established.
Specifically, the correlation function is calculated by using a dynamic time-warping algorithm to obtain the time offset between each seismic trace and other seismic traces, so as to establish the paired correlations between all time points or depth points in two seismic sequences, and finally, multi-scale dip angle information is obtained according to the correlations.
Step S143, determining a multi-scale tilt angle according to the pairwise correlation.
Example two
Fig. 2 is a schematic flow chart of a seismic horizon extraction method according to a second embodiment of the invention. This example is a specific experiment. As shown in fig. 2, the method includes:
step S21, selecting a plurality of control points based on the target horizon of the seismic data.
This embodiment illustrates 1 target horizon in the seismic data and 1 control point. These control points provide constraints in automatic horizon picking, and the positions of the control points and target horizons are shown in fig. 3, where fig. 3 is a two-dimensional actual seismic profile data with a lateral sampling number of 380.
Step S22, the seismic data can be intercepted along the time or depth direction according to the memory requirement, and the seismic data near the target horizon is obtained.
This step helps to increase the memory and computational consumption of horizon picking when subsequently computing local dip and performing seismic horizon picking.
And S23, performing feature decomposition on the intercepted seismic data based on the structure tensor to obtain the local dip angle of the seismic data.
Wherein fig. 4 illustrates the local dip obtained by performing a feature decomposition on the seismic data of fig. 3. For three-dimensional seismic data, the local dip angle in the vertical direction is included, as well as the local dip angle in the horizontal direction.
Step S24, a fault is picked up or linearity or flatness is calculated for the seismic data.
The fault, or linearity, or flatness of the pick-up may be a constraint of discontinuities when horizon pick-up. Specifically, if no faults are picked up, it may be selected to calculate linearity (for two-dimensional seismic data) or flatness (for three-dimensional seismic data) on the seismic data. Fig. 5 illustrates the linearity of the corresponding seismic data of fig. 3, which can be found to highlight reflection locations, but lower in amplitude at fault locations. This linearity is analogous to the fault picked up as a constraint for discontinuities in horizon picking. Besides the weighting, the picked fault or the calculated linearity (flatness) can also be used for avoiding the seismic traces at the fault position when the seismic traces do multi-scale cross-correlation, so that the effectiveness of the cross-correlation of the seismic traces is ensured.
And S25, selecting seismic channels corresponding to the coarse meshes, and cutting windows of the seismic channels.
Specifically, the calculation efficiency can be improved by cutting windows of the seismic channels. The extracted seismic traces are shown in fig. 6, and fig. 7 is a schematic diagram of the waveform of the seismic traces after being leveled according to time sequence.
Step S26, selecting seismic channels with different distances for the truncated window seismic channels corresponding to the selected coarse grid, and performing multi-scale cross-correlation by a dynamic normalization method to obtain a correlation function.
Wherein fig. 8 shows a form of multi-scale cross-correlation traces, including cross-correlations between equally spaced pairs of traces, and cross-correlations of reference traces (traces corresponding to control points) with other traces.
And step S27, obtaining relative displacement according to the correlation function, finally obtaining a leveled seismic trace, and calculating to obtain a multi-scale dip angle.
The dynamically normalized seismic traces are shown in fig. 9, and the leveled seismic traces are shown in fig. 10.
And step S28, combining the local dip angle with the multi-scale dip angle, and continuously and iteratively updating the horizon to obtain the final seismic horizon.
Where iteratively updated horizons are shown in fig. 11, it can be found that the updated horizons track phase consistency well.
Example III
Fig. 12 shows a schematic structural diagram of a seismic horizon extraction apparatus according to an embodiment of the invention. As shown in fig. 12, the apparatus includes: a control point selection module 31, a local dip determination module 32, an initial horizon determination module 33, a multi-scale dip determination module 34, and a seismic horizon determination module 35; wherein,,
the control point selection module 31 is configured to select a preset control point from a target horizon of the seismic data, and intercept the seismic data based on the target horizon and the preset control point to obtain target seismic data;
the local dip determination module 32 is configured to determine a local dip of the seismic image based on the structure tensor of the target seismic data;
the initial horizon determination module 33 is configured to perform linear interpolation on the target seismic data according to the preset control points to obtain an initial seismic horizon;
the multi-scale dip angle determining module 34 is configured to select a preset number of seismic traces according to the initial seismic horizon, and determine multi-scale dip angles based on a cross-correlation function between every two preset number of seismic traces;
the seismic horizon determination module 35 is configured to obtain a final seismic horizon based on the local dip and the multi-scale dip.
Further, the local inclination determining module 32 is specifically configured to: performing eigenvalue and eigenvector decomposition on the structure tensor of the target seismic data; and determining the local dip angle of the seismic image according to the feature vector.
Further, the seismic horizon determination module 35 is specifically configured to: determining weights according to faults or linearity or flatness of the target seismic data; determining a seismic horizon extraction equation according to the weight, the local dip angle and the multi-scale dip angle; and obtaining a final seismic horizon according to the seismic horizon extraction equation.
Further, the seismic horizon determination module 35 is further configured to: performing eigenvalue and eigenvector decomposition on the structure tensor of the target seismic data; and determining the linearity or flatness of the seismic image according to the characteristic value.
Further, the multi-scale inclination angle determining module 34 is specifically configured to: determining a seismic trace based on control points in the initial seismic horizon and a preset interval; and screening out the seismic traces at the fault positions when the preset number of seismic traces are selected.
Further, the multi-scale inclination angle determining module 34 is specifically configured to: arranging the preset number of seismic channels according to a time sequence, and performing cross-correlation on each seismic channel and other seismic channels to obtain a correlation function; determining the time offset between each seismic channel and other seismic channels according to the correlation function, and establishing paired correlations between all time points or depth points between every two seismic channels; and determining a multi-scale dip angle according to the pairwise correlation.
Further, the control points are distributed along a direction of construction of the seismic data.
The seismic horizon extraction apparatus according to the present embodiment is configured to perform the seismic horizon extraction methods according to the first and second embodiments, and the working principle is similar to the technical effect, and is not repeated here.
Example IV
A fourth embodiment of the present invention provides a non-volatile computer storage medium storing at least one executable instruction for performing the seismic horizon extraction method according to any of the method embodiments described above.
Example five
Fig. 13 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention. The specific embodiments of the present invention are not limited to specific implementations of electronic devices.
As shown in fig. 13, the electronic device may include: a processor (processor) 302, a communication interface (Communications Interface) 304, a memory (memory) 306, and a communication bus 308.
Wherein: processor 302, communication interface 304, and memory 306 perform communication with each other via communication bus 308. A communication interface 304 for communicating with network elements of other devices, such as clients or other servers. Processor 302 is configured to execute program 310, and may specifically perform relevant steps in the method embodiments described above.
In particular, program 310 may include program code including computer-operating instructions.
The processor 302 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the electronic device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
Memory 306 for storing programs 310. Memory 306 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
Program 310 may be specifically configured to cause processor 302 to perform the seismic horizon extraction method of any of the method embodiments described above.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.
Claims (10)
1. A method of seismic horizon extraction comprising:
selecting a preset control point from a target horizon of the seismic data, and intercepting the seismic data based on the target horizon and the preset control point to obtain target seismic data;
determining a local dip angle of the seismic image according to the structure tensor of the target seismic data;
performing linear interpolation on the target seismic data according to the preset control points to obtain an initial seismic horizon;
selecting a preset number of seismic channels according to the initial seismic horizon, and determining a multi-scale dip angle based on a cross-correlation function between every two preset number of seismic channels;
and obtaining a final seismic horizon according to the local dip angle and the multi-scale dip angle.
2. The method of claim 1, wherein said determining the local dip of the seismic image from the structure tensor of the target seismic data comprises:
performing eigenvalue and eigenvector decomposition on the structure tensor of the target seismic data;
and determining the local dip angle of the seismic image according to the feature vector.
3. The method of claim 1, wherein the deriving a final seismic horizon from the local dip and the multi-scale dip comprises:
determining weights according to faults or linearity or flatness of the target seismic data;
determining a seismic horizon extraction equation according to the weight, the local dip angle and the multi-scale dip angle;
and obtaining a final seismic horizon according to the seismic horizon extraction equation.
4. A method according to claim 3, characterized in that the method further comprises:
performing eigenvalue and eigenvector decomposition on the structure tensor of the target seismic data;
and determining the linearity or flatness of the seismic image according to the characteristic value.
5. The method of any of claims 1-4, wherein selecting a predetermined number of seismic traces from the initial seismic horizon comprises:
determining a seismic trace based on control points in the initial seismic horizon and a preset interval;
and screening out the seismic traces at the fault positions when the preset number of seismic traces are selected.
6. The method of any of claims 1-4, wherein determining a multi-scale dip based on a cross-correlation function between the predetermined number of seismic traces comprises:
arranging the preset number of seismic channels according to a time sequence, and performing cross-correlation on each seismic channel and other seismic channels to obtain a correlation function;
determining the time offset between each seismic channel and other seismic channels according to the correlation function, and establishing paired correlations between all time points or depth points between every two seismic channels;
and determining a multi-scale dip angle according to the pairwise correlation.
7. The method of any of claims 1-4, wherein the control points are distributed along a direction of construction of the seismic data.
8. A seismic horizon extraction apparatus comprising:
the control point selection module is used for selecting a preset control point from a target horizon of the seismic data, and intercepting the seismic data based on the target horizon and the preset control point to obtain the target seismic data;
the local dip angle determining module is used for determining the local dip angle of the seismic image according to the structure tensor of the target seismic data;
the initial horizon determination module is used for carrying out linear interpolation on the target seismic data according to the preset control points to obtain an initial seismic horizon;
the multi-scale dip angle determining module is used for selecting a preset number of seismic channels according to the initial seismic horizon and determining multi-scale dip angles based on cross-correlation functions between every two preset number of seismic channels;
and the seismic horizon determination module is used for obtaining a final seismic horizon according to the local dip angle and the multi-scale dip angle.
9. An electronic device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the seismic horizon extraction method of any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the seismic horizon extraction method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310429289.8A CN116400412A (en) | 2023-04-20 | 2023-04-20 | Seismic horizon extraction method and device, electronic equipment and computer storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310429289.8A CN116400412A (en) | 2023-04-20 | 2023-04-20 | Seismic horizon extraction method and device, electronic equipment and computer storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116400412A true CN116400412A (en) | 2023-07-07 |
Family
ID=87015882
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310429289.8A Pending CN116400412A (en) | 2023-04-20 | 2023-04-20 | Seismic horizon extraction method and device, electronic equipment and computer storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116400412A (en) |
-
2023
- 2023-04-20 CN CN202310429289.8A patent/CN116400412A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7844402B2 (en) | System and method for fault identification | |
| CA2683618C (en) | Inverse-vector method for smoothing dips and azimuths | |
| Fedi et al. | Multiridge analysis of potential fields: Geometric method and reduced Euler deconvolution | |
| AU2009308037B2 (en) | Tracking geologic object and detecting geologic anomalies in exploration seismic data volume | |
| WO2014164970A2 (en) | Methods and systems for locating seismic events | |
| US10274623B2 (en) | Determining displacement between seismic images using optical flow | |
| US10451757B2 (en) | Determining displacement between seismic images using optical flow | |
| WO2022232572A1 (en) | Method and system for high resolution least-squares reverse time migration | |
| Mahadik et al. | Fault detection and optimization in seismic dataset using multiscale fusion of a geometric attribute | |
| US8340912B2 (en) | Seismic attributes for structural analysis | |
| Aitken et al. | Semiautomated quantification of the influence of data richness on confidence in the geologic interpretation of aeromagnetic maps | |
| CN116400412A (en) | Seismic horizon extraction method and device, electronic equipment and computer storage medium | |
| Xing et al. | Application of 3D stereotomography to the deep-sea data acquired in the South China Sea: a tomography inversion case | |
| US11573347B2 (en) | Computing program product and method that interpolates wavelets coefficients and estimates spatial varying wavelets using the covariance interpolation method in the data space over a survey region having multiple well locations | |
| US20240111067A1 (en) | Faulted seismic horizon mapping | |
| Koyan et al. | 3D ground-penetrating radar data analysis and interpretation using attributes based on the gradient structure tensor | |
| CN117784263A (en) | Ferromagnetic target positioning method and device and related equipment | |
| CN115629423A (en) | Magnetic moment calculation method and device based on scalar magnetic anomaly and related equipment | |
| CN116011306A (en) | Velocity modeling method and device based on plane source seismic data and electronic equipment | |
| James et al. | Rapid structural framework generation for reservoir property propagation and modeling in complex structures | |
| FitzGerald | Full Tensor Magnetic Gradiometry |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |